6307
2888
weka.FilteredClassifier_HoeffdingTree
weka.classifiers.meta.FilteredClassifier
1
Weka_3.8.1_12647
Weka implementation of FilteredClassifier
2017-04-17T15:23:23
English
Weka_3.8.1
-do-not-check-capabilities
flag
If set, classifier capabilities are not checked before classifier is built
(use with caution).
-doNotMakeSplitPointActualValue
flag
Do not make split point actual value.
A
flag
Laplace smoothing for predicted probabilities.
B
flag
Use binary splits only.
C
option
Set confidence threshold for pruning.
(default 0.25)
D
flag
Output binary attributes for discretized attributes.
E
flag
Use better encoding of split point for MDL.
F
option
weka.filters.supervised.attribute.Discretize -R first-last -precision 6
Full class name of filter to use, followed
by filter options.
eg: "weka.filters.unsupervised.attribute.Remove -V -R 1,2"
J
flag
Do not use MDL correction for info gain on numeric attributes.
K
flag
Use Kononenko's MDL criterion.
L
flag
Do not clean up after the tree has been built.
M
option
Set minimum number of instances per leaf.
(default 2)
N
option
Set number of folds for reduced error
pruning. One fold is used as pruning set.
(default 3)
O
flag
Do not collapse tree.
Q
option
Seed for random data shuffling (default 1).
R
flag
Use reduced error pruning.
S
flag
Do not perform subtree raising.
U
flag
Use unpruned tree.
V
flag
Invert matching sense of column indexes.
W
baselearner
weka.classifiers.trees.HoeffdingTree
Full name of base classifier.
(default: weka.classifiers.trees.J48)
Y
flag
Use bin numbers rather than ranges for discretized attributes.
batch-size
option
The desired batch size for batch prediction (default 100).
num-decimal-places
option
The number of decimal places for the output of numbers in the model (default 2).
output-debug-info
flag
If set, classifier is run in debug mode and
may output additional info to the console
precision
option
Precision for bin boundary labels.
(default = 6 decimal places).
W
5943
2747
weka.HoeffdingTree
weka.classifiers.trees.HoeffdingTree
6
Weka_3.8.1_11006
Geoff Hulten, Laurie Spencer, Pedro Domingos: Mining time-changing data streams. In: ACM SIGKDD Intl. Conf. on Knowledge Discovery and Data Mining, 97-106, 2001.
2017-03-30T15:20:36
English
Weka_3.8.1
E
option
1.0E-7
The allowable error in a split decision - values closer to zero will take longer to decide
(default = 1e-7)
G
option
200.0
Grace period - the number of instances a leaf should observe between split attempts
(default = 200)
H
option
0.05
Threshold below which a split will be forced to break ties
(default = 0.05)
L
option
2
The leaf prediction strategy to use. 0 = majority class, 1 = naive Bayes, 2 = naive Bayes adaptive.
(default = 2)
M
option
0.01
Minimum fraction of weight required down at least two branches for info gain splitting
(default = 0.01)
N
option
0.0
The number of instances (weight) a leaf should observe before allowing naive Bayes to make predictions (NB or NB adaptive only)
(default = 0)
P
flag
Print leaf models when using naive Bayes at the leaves.
S
option
1
The splitting criterion to use. 0 = Gini, 1 = Info gain
(default = 1)
Verified_Supervised_Classification
weka
weka_3.8.1
https://api.openml.org/data/download/4699324/weka.classifiers.trees.HoeffdingTree641263798600590420.class
class
e13ea9ef1e91673a0473e7831a351c34
Verified_Supervised_Classification
weka
weka_3.8.1
https://api.openml.org/data/download/5035525/weka.classifiers.meta.FilteredClassifier511884703049568570.class
class
d36a54655368c6b3970f0bd7a40dda83